{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost\n",
       "0              Budweiser       144      15      4.7  0.43\n",
       "1                Schlitz       151      19      4.9  0.43\n",
       "2              Lowenbrau       157      15      0.9  0.48\n",
       "3            Kronenbourg       170       7      5.2  0.73\n",
       "4               Heineken       152      11      5.0  0.77\n",
       "5          Old_Milwaukee       145      23      4.6  0.28\n",
       "6             Augsberger       175      24      5.5  0.40\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42\n",
       "8            Miller_Lite        99      10      4.3  0.43\n",
       "9        Budweiser_Light       113       8      3.7  0.40\n",
       "10                 Coors       140      18      4.6  0.44\n",
       "11           Coors_Light       102      15      4.1  0.46\n",
       "12        Michelob_Light       135      11      4.2  0.50\n",
       "13                 Becks       150      19      4.7  0.76\n",
       "14                 Kirin       149       6      5.0  0.79\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38\n",
       "16                 Hamms       139      19      4.4  0.43\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46\n",
       "19         Schlitz_Light        97       7      4.2  0.47"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# beer dataset\n",
    "import pandas as pd\n",
    "beer = pd.read_csv('data.txt', sep=' ')\n",
    "beer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = beer[[\"calories\",\"sodium\",\"alcohol\",\"cost\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "km = KMeans(n_clusters=3).fit(X)\n",
    "km2 = KMeans(n_clusters=2).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 2, 0, 0, 2, 1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster  cluster2\n",
       "0              Budweiser       144      15      4.7  0.43        0         1\n",
       "1                Schlitz       151      19      4.9  0.43        0         1\n",
       "2              Lowenbrau       157      15      0.9  0.48        0         1\n",
       "3            Kronenbourg       170       7      5.2  0.73        0         1\n",
       "4               Heineken       152      11      5.0  0.77        0         1\n",
       "5          Old_Milwaukee       145      23      4.6  0.28        0         1\n",
       "6             Augsberger       175      24      5.5  0.40        0         1\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        0         1\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        0         1\n",
       "16                 Hamms       139      19      4.4  0.43        0         1\n",
       "10                 Coors       140      18      4.6  0.44        0         1\n",
       "14                 Kirin       149       6      5.0  0.79        0         1\n",
       "12        Michelob_Light       135      11      4.2  0.50        0         1\n",
       "13                 Becks       150      19      4.7  0.76        0         1\n",
       "9        Budweiser_Light       113       8      3.7  0.40        1         0\n",
       "8            Miller_Lite        99      10      4.3  0.43        1         0\n",
       "11           Coors_Light       102      15      4.1  0.46        1         0\n",
       "19         Schlitz_Light        97       7      4.2  0.47        1         0\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        2         0\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        2         0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer['cluster'] = km.labels_\n",
    "beer['cluster2'] = km2.labels_\n",
    "beer.sort_values('cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "%matplotlib inline\n",
    "\n",
    "cluster_centers = km.cluster_centers_\n",
    "\n",
    "cluster_centers_2 = km2.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.521429</td>\n",
       "      <td>0.520714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102.75</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.075000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.00</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         calories  sodium   alcohol      cost  cluster2\n",
       "cluster                                                \n",
       "0          150.00    17.0  4.521429  0.520714         1\n",
       "1          102.75    10.0  4.075000  0.440000         0\n",
       "2           70.00    10.5  2.600000  0.420000         0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer.groupby(\"cluster\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91.833333</td>\n",
       "      <td>10.166667</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.521429</td>\n",
       "      <td>0.520714</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            calories     sodium   alcohol      cost   cluster\n",
       "cluster2                                                     \n",
       "0          91.833333  10.166667  3.583333  0.433333  1.333333\n",
       "1         150.000000  17.000000  4.521429  0.520714  0.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer.groupby(\"cluster2\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "centers = beer.groupby(\"cluster\").mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "colors = np.array(['red', 'green', 'blue', 'yellow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x18a25af4ac8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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OluhUjuQihRERaTbunziRowYNYvfPPuOAvDzmu7MxL4/HHnyQtnUY2ejbty833ngj11xz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18a25ac9898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(beer[\"calories\"], beer[\"alcohol\"],c=colors[beer[\"cluster\"]])\n",
    "\n",
    "plt.scatter(centers.calories, centers.alcohol, linewidths=3, marker='+', s=300, c='black')\n",
    "\n",
    "plt.xlabel(\"Calories\")\n",
    "plt.ylabel(\"Alcohol\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x18a25b67e80>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8kikRgauvhssuKzqw3GcfeOcdnQHNVZ07wzHHwNZbZzqS7LDNNmF/3mefMN2k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TN8+HC6du1ap620tJTS0lIAysrgT3+CyZOhf38YMQJ23DEX6aT8Kisro6ysrE7b1KlTAYgRrrwS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\njamSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18a25b675f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(beer[[\"calories\",\"sodium\",\"alcohol\",\"cost\"]],s=100, alpha=1, c=colors[beer[\"cluster\"]], figsize=(10,10))\n",
    "plt.suptitle(\"With 3 centroids initialized\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x18a2613c710>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SKRERERERkSgomRIREREREYmCkikREREREZEoKJkSERERERGJgpIpERERERGRKCiZEhER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HPHu5gsBBw54vxftarvt33oEOzd67nPw303NT2V1IzUU14zrKzp0bgHs4bPcjuGiEjg2LaNChkw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18a2613c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(beer[[\"calories\",\"sodium\",\"alcohol\",\"cost\"]],s=100, alpha=1, c=colors[beer[\"cluster2\"]], figsize=(10,10))\n",
    "plt.suptitle(\"With 2 centroids initialized\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.38791334,  0.00779468,  0.43380786, -0.45682969],\n",
       "       [ 0.6250656 ,  0.63136906,  0.62241997, -0.45682969],\n",
       "       [ 0.82833896,  0.00779468, -3.14982226, -0.10269815],\n",
       "       [ 1.26876459, -1.23935408,  0.90533814,  1.66795955],\n",
       "       [ 0.65894449, -0.6157797 ,  0.71672602,  1.95126478],\n",
       "       [ 0.42179223,  1.25494344,  0.3395018 , -1.5192243 ],\n",
       "       [ 1.43815906,  1.41083704,  1.1882563 , -0.66930861],\n",
       "       [ 0.55730781,  1.87851782,  0.43380786, -0.52765599],\n",
       "       [-1.1366369 , -0.7716733 ,  0.05658363, -0.45682969],\n",
       "       [-0.66233238, -1.08346049, -0.5092527 , -0.66930861],\n",
       "       [ 0.25239776,  0.47547547,  0.3395018 , -0.38600338],\n",
       "       [-1.03500022,  0.00779468, -0.13202848, -0.24435076],\n",
       "       [ 0.08300329, -0.6157797 , -0.03772242,  0.03895447],\n",
       "       [ 0.59118671,  0.63136906,  0.43380786,  1.88043848],\n",
       "       [ 0.55730781, -1.39524768,  0.71672602,  2.0929174 ],\n",
       "       [-2.18688263,  0.00779468, -1.82953748, -0.81096123],\n",
       "       [ 0.21851887,  0.63136906,  0.15088969, -0.45682969],\n",
       "       [ 0.38791334,  1.41083704,  0.62241997, -0.45682969],\n",
       "       [-2.05136705, -1.39524768, -1.26370115, -0.24435076],\n",
       "       [-1.20439469, -1.23935408, -0.03772242, -0.17352445]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=3).fit(X_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "      <th>scaled_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster  cluster2  \\\n",
       "0              Budweiser       144      15      4.7  0.43        0         1   \n",
       "1                Schlitz       151      19      4.9  0.43        0         1   \n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        0         1   \n",
       "16                 Hamms       139      19      4.4  0.43        0         1   \n",
       "5          Old_Milwaukee       145      23      4.6  0.28        0         1   \n",
       "6             Augsberger       175      24      5.5  0.40        0         1   \n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        0         1   \n",
       "10                 Coors       140      18      4.6  0.44        0         1   \n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        2         0   \n",
       "12        Michelob_Light       135      11      4.2  0.50        0         1   \n",
       "11           Coors_Light       102      15      4.1  0.46        1         0   \n",
       "9        Budweiser_Light       113       8      3.7  0.40        1         0   \n",
       "8            Miller_Lite        99      10      4.3  0.43        1         0   \n",
       "2              Lowenbrau       157      15      0.9  0.48        0         1   \n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        2         0   \n",
       "19         Schlitz_Light        97       7      4.2  0.47        1         0   \n",
       "13                 Becks       150      19      4.7  0.76        0         1   \n",
       "14                 Kirin       149       6      5.0  0.79        0         1   \n",
       "4               Heineken       152      11      5.0  0.77        0         1   \n",
       "3            Kronenbourg       170       7      5.2  0.73        0         1   \n",
       "\n",
       "    scaled_cluster  \n",
       "0                0  \n",
       "1                0  \n",
       "17               0  \n",
       "16               0  \n",
       "5                0  \n",
       "6                0  \n",
       "7                0  \n",
       "10               0  \n",
       "15               1  \n",
       "12               1  \n",
       "11               1  \n",
       "9                1  \n",
       "8                1  \n",
       "2                1  \n",
       "18               1  \n",
       "19               1  \n",
       "13               2  \n",
       "14               2  \n",
       "4                2  \n",
       "3                2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer[\"scaled_cluster\"] = km.labels_\n",
    "beer.sort_values(\"scaled_cluster\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the \"characteristics\" of each cluster?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scaled_cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148.375</td>\n",
       "      <td>21.125</td>\n",
       "      <td>4.7875</td>\n",
       "      <td>0.4075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>105.375</td>\n",
       "      <td>10.875</td>\n",
       "      <td>3.3250</td>\n",
       "      <td>0.4475</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>155.250</td>\n",
       "      <td>10.750</td>\n",
       "      <td>4.9750</td>\n",
       "      <td>0.7625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                calories  sodium  alcohol    cost  cluster  cluster2\n",
       "scaled_cluster                                                      \n",
       "0                148.375  21.125   4.7875  0.4075      0.0      1.00\n",
       "1                105.375  10.875   3.3250  0.4475      1.0      0.25\n",
       "2                155.250  10.750   4.9750  0.7625      0.0      1.00"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer.groupby(\"scaled_cluster\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000018A279F8F28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A282989B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27B5E2E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27B94F60>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27BE41D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27C19F28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27C61F60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27C71C88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27CF1860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27D3B7B8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27D7C5C0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27DC6F98>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27E02748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27E4FEB8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27E8D588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A27ED47B8>]], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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MGgSnnBLaTFJmXngBLrwwfzk2Fnr2hF4HXS7csKEb+udLIJ93VEyJCJf4uKSmUqWiL0QS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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18a27b03d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(X, c=colors[beer.scaled_cluster], alpha=1, figsize=(10,10), s=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 聚类评估：轮廓系数（Silhouette Coefficient ）\n",
    "\n",
    "<img src=\"1.png\" alt=\"FAO\" width=\"490\">\n",
    "\n",
    "- 计算样本i到同簇其他样本的平均距离ai。ai 越小，说明样本i越应该被聚类到该簇。将ai 称为样本i的簇内不相似度。\n",
    "- 计算样本i到其他某簇Cj 的所有样本的平均距离bij，称为样本i与簇Cj 的不相似度。定义为样本i的簇间不相似度：bi =min{bi1, bi2, ..., bik}\n",
    "\n",
    "\n",
    "* si接近1，则说明样本i聚类合理\n",
    "* si接近-1，则说明样本i更应该分类到另外的簇\n",
    "* 若si 近似为0，则说明样本i在两个簇的边界上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.179780680894 0.673177504646\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "score_scaled = metrics.silhouette_score(X,beer.scaled_cluster)\n",
    "score = metrics.silhouette_score(X,beer.cluster)\n",
    "print(score_scaled, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.69176560340794857,\n",
       " 0.67317750464557957,\n",
       " 0.58570407211277953,\n",
       " 0.42254873351720201,\n",
       " 0.4559182167013377,\n",
       " 0.43776116697963124,\n",
       " 0.38946337473125997,\n",
       " 0.39746405172426014,\n",
       " 0.33061511213823314,\n",
       " 0.34131096180393328,\n",
       " 0.34597752371272478,\n",
       " 0.31221439248428434,\n",
       " 0.30707782144770296,\n",
       " 0.31834561839139497,\n",
       " 0.28495140011748982,\n",
       " 0.23498077333071996,\n",
       " 0.15880910174962809,\n",
       " 0.084230513801511767]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = []\n",
    "for k in range(2,20):\n",
    "    labels = KMeans(n_clusters=k).fit(X).labels_\n",
    "    score = metrics.silhouette_score(X, labels)\n",
    "    scores.append(score)\n",
    "\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x18a288239e8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZQ+DPQAfg7zWITURi1LdvqL90xx1w2WVxRyMiskbUpdJ/Aw4FLjCzIcAPAGY2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\nXnrppZde0V6D85knJLXn5TngHOAAYHK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18a278a34a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list(range(2,20)), scores)\n",
    "plt.xlabel(\"Number of Clusters Initialized\")\n",
    "plt.ylabel(\"Sihouette Score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  DBSCAN clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "db = DBSCAN(eps=10, min_samples=2).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = db.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "      <th>scaled_cluster</th>\n",
       "      <th>cluster_db</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster  cluster2  \\\n",
       "9        Budweiser_Light       113       8      3.7  0.40        1         0   \n",
       "3            Kronenbourg       170       7      5.2  0.73        0         1   \n",
       "6             Augsberger       175      24      5.5  0.40        0         1   \n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        0         1   \n",
       "16                 Hamms       139      19      4.4  0.43        0         1   \n",
       "14                 Kirin       149       6      5.0  0.79        0         1   \n",
       "13                 Becks       150      19      4.7  0.76        0         1   \n",
       "12        Michelob_Light       135      11      4.2  0.50        0         1   \n",
       "10                 Coors       140      18      4.6  0.44        0         1   \n",
       "0              Budweiser       144      15      4.7  0.43        0         1   \n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        0         1   \n",
       "5          Old_Milwaukee       145      23      4.6  0.28        0         1   \n",
       "4               Heineken       152      11      5.0  0.77        0         1   \n",
       "2              Lowenbrau       157      15      0.9  0.48        0         1   \n",
       "1                Schlitz       151      19      4.9  0.43        0         1   \n",
       "8            Miller_Lite        99      10      4.3  0.43        1         0   \n",
       "11           Coors_Light       102      15      4.1  0.46        1         0   \n",
       "19         Schlitz_Light        97       7      4.2  0.47        1         0   \n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        2         0   \n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        2         0   \n",
       "\n",
       "    scaled_cluster  cluster_db  \n",
       "9                1          -1  \n",
       "3                2          -1  \n",
       "6                0          -1  \n",
       "17               0           0  \n",
       "16               0           0  \n",
       "14               2           0  \n",
       "13               2           0  \n",
       "12               1           0  \n",
       "10               0           0  \n",
       "0                0           0  \n",
       "7                0           0  \n",
       "5                0           0  \n",
       "4                2           0  \n",
       "2                1           0  \n",
       "1                0           0  \n",
       "8                1           1  \n",
       "11               1           1  \n",
       "19               1           1  \n",
       "15               1           2  \n",
       "18               1           2  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer['cluster_db'] = labels\n",
    "beer.sort_values('cluster_db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "      <th>scaled_cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_db</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>152.666667</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>146.250000</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>4.383333</td>\n",
       "      <td>0.513333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.333333</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>0.453333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              calories     sodium   alcohol      cost   cluster  cluster2  \\\n",
       "cluster_db                                                                  \n",
       "-1          152.666667  13.000000  4.800000  0.510000  0.333333  0.666667   \n",
       " 0          146.250000  17.250000  4.383333  0.513333  0.000000  1.000000   \n",
       " 1           99.333333  10.666667  4.200000  0.453333  1.000000  0.000000   \n",
       " 2           70.000000  10.500000  2.600000  0.420000  2.000000  0.000000   \n",
       "\n",
       "            scaled_cluster  \n",
       "cluster_db                  \n",
       "-1                1.000000  \n",
       " 0                0.666667  \n",
       " 1                1.000000  \n",
       " 2                1.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beer.groupby('cluster_db').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000018A278A3940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A284C56D8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A28501CF8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A28550080>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000018A2856C588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A285D1F60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A286211D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A2865AF98>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000018A286AABA8>,\n",
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A287396A0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000018A287BC358>,\n",
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018A28BBC470>]], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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twP/T2R2HEB4MIbwHjAOeCiGsadz0MNAH+HkI4bUQwqshhCGN274F9AshrAWe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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18a2844c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(X, c=colors[beer.cluster_db], figsize=(10,10), s=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
